1. The Power of Analytics Applying and Implementing Analytics – How to, When to, and Why May 23, 2016 Session 2 Presented by Raymond Wedgeworth, Director, Data Analytics and Systems Group, Center for Program Integrity, Centers for Medicare and Medicaid Services (CMS) Analytics Implementation Case #4: Inappropriate Payments and Questionable Billing for Ambulance Services

2. CMS covers 100 million people through Medicare, Medicaid, the Children’s Health Insurance Program and the Health Insurance Marketplace. The Center for Program Integrity (CPI) is a Center within CMS. One of the missions of the CPI is to identify fraud, waste, and abuse in the Medicare program. Often vulnerabilities are identified and we attempt to develop predictive analytics that will catch fraudulent actors and provide that information back to the investigative community for potential administrative and/or legal actions. OIG published a report that Ambulance companies were inappropriately paid $24 million in the first half of 2012. For example, Medicare paid $17 million for transports that were to or from non-covered destinations such as physicians’ offices. What are our analytics project objectives? Analytics Implementation Case #4: Inappropriate Payments and Questionable Billing for Ambulance Services at CMS In order for analytics to lead to actionable information, it needs to be combined with policy and knowledge held by the investigative community. The challenge is developing efficient and actionable models that detect fraud, waste, and abuse. For this case, we are looking for a fresh perspective on an approach for developing a predictive model for detecting inappropriate/questionable billing for ambulance services. Case Introduction 2

3. Analytics Implementation Case #4: Inappropriate Payments and Questionable Billing for Ambulance Services at CMS Current Situation at CMS CMS’ analytic capability is extensive: Two modeling and analytics contractors Models developed primarily using SAS; used in a fraud prevention system (FPS); FPS deploys the models that present alerts back to the investigative community; The FPS streams 4.5 million claims/day and based on that info alerts are generated at the provider level. Claims data provides: Ambulance services Date of service Beneficiary receiving service Allowed payment amount Type of service Procedure code(s) To and From codes (in the instance of ambulance there are transportation codes indicating “To”, where the patient was transported to and “From” where the patient was picked up) National Provider Identifier (NPI) All institutional claims (e.g. hospitals, skilled nursing facilities) and all physician claims Non-claims data includes a listing of historic known fraudulent providers which includes the NPI. 3

4. Challenges, Risks, and Roadblocks at CMS The models need to produce outputs that are actionable by the investigative community Outputs and resulting actions must adhere to anti-fraud authorities and policies Risk of producing models that are driven by analytics, but do not result in actionable information Analytics Implementation Case #4: Inappropriate Payments and Questionable Billing for Ambulance Services at CMS 4

5. Case Solution - Provide the following: 1.Recommendations: What are the 3-5 priority activities/actions CMS should undertake to develop an actionable, predictive model for detecting inappropriate or questionable billing for ambulance services? 2.What will these activities cost CMS (ROM only)? 3.In what sequence or priority should these activities be planned and/or executed? 4.What other key considerations should CMS take into account? Analytics Implementation Case #4: Inappropriate Payments and Questionable Billing for Ambulance Services at CMS 5